CN115120209A - Livestock data analysis system and data analysis method and device thereof - Google Patents
Livestock data analysis system and data analysis method and device thereof Download PDFInfo
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- 244000144972 livestock Species 0.000 title claims abstract description 110
- 238000007405 data analysis Methods 0.000 title claims abstract description 42
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- 230000009471 action Effects 0.000 claims abstract description 29
- 238000012545 processing Methods 0.000 claims abstract description 17
- 238000003062 neural network model Methods 0.000 claims abstract description 15
- 230000036760 body temperature Effects 0.000 claims description 13
- 230000012173 estrus Effects 0.000 claims description 9
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- 230000004044 response Effects 0.000 description 3
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- 238000004590 computer program Methods 0.000 description 2
- 230000001121 heart beat frequency Effects 0.000 description 2
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- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
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- A61B5/02055—Simultaneously evaluating both cardiovascular condition and temperature
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Abstract
The application discloses a livestock data analysis system and a data analysis method and device thereof. Each electronic ear tag is configured on an ear of a livestock, and is used for acquiring and continuously recording body parameters and/or current position information of the livestock and sending the body parameters and/or the current position information to a server through the Internet of things; the server is used for generating action tracks and/or movement frequencies of the livestock based on all current position parameters, then processing the body parameters, the action tracks and/or the movement tracks based on the neural network model to obtain the body state of the livestock, and sending the body state to the user. Through this technical scheme, can in time master the health state of every livestock in extensive herd, and no longer rely on the personnel of breeding to look over one by one of every livestock to avoided bringing the loss for the raiser because can't in time master the health condition or other information of livestock.
Description
Technical Field
The application relates to the technical field of livestock raising, in particular to a livestock data analysis system and a data analysis method and device thereof.
Background
For large-scale livestock breeding, the health state and other state pairs of livestock need to be mastered in time so as to take treatment measures in time according to the health state and other states. In the past, the livestock can be known in detail only by checking the livestock on site by breeding personnel, and then operations such as disease prevention and treatment are carried out according to the known information. This approach is not problematic for small scale farming, but is not desirable for large scale farming, and serious problems can occur and cause losses to farmers if timely measures are not collected.
Disclosure of Invention
In view of this, the present application provides a system and a method and a device for analyzing livestock data, so as to avoid loss of farmers due to incapability of timely grasping health status or other information of livestock.
In order to achieve the above object, the following solutions are proposed:
a livestock data analysis system comprising a server and a plurality of electronic ear tags connected to the server, wherein:
each electronic ear tag is used for being configured on an ear of a livestock, and is used for acquiring and continuously recording body parameters and/or current position information of the livestock and sending the body parameters and/or the current position information to the server through the Internet of things;
the server is used for receiving the body parameters and/or the current position parameters, generating action tracks and/or motion frequencies of the livestock based on all the current position parameters, processing the body parameters, the action tracks and/or the motion tracks based on a neural network model to obtain body states of the livestock, and sending the body states to users.
Optionally, the electronic ear tag is configured to detect a body temperature and/or a heart rate of the livestock, and obtain the body parameter by combining the body temperature and/or the heart rate.
A data analysis method for use in the livestock data analysis system as described above, said data analysis method comprising the steps of:
acquiring body parameters and/or current position information of livestock;
summarizing the current position information to obtain the action track and/or the movement frequency of the livestock;
and processing the body parameters, the action tracks and/or the motion frequency based on a neural network model to obtain the body state of the livestock.
Optionally, the physical parameter comprises body temperature and/or heart rate.
Optionally, the physical state comprises a healthy state, a developmental state, or an oestrus state.
A data analysis method applied to the livestock data analysis system as described above, characterized in that the data analysis device comprises:
a parameter acquisition module configured to acquire body parameters and/or current position information of the livestock;
the position processing module is configured to perform summary processing on the current position information to obtain action tracks and/or movement frequencies of the livestock;
an analysis execution module configured to process the body parameters, the action tracks and/or the motion frequency based on a neural network model to obtain the body state of the livestock.
Optionally, the physical parameter comprises body temperature and/or heart rate.
Optionally, the physical state comprises a healthy state, a developmental state, or an oestrus state.
According to the technical scheme, the livestock data analysis system comprises a server and a plurality of electronic ear tags connected with the server. Each electronic ear tag is used for being arranged on the ear of one livestock, and is used for acquiring and continuously recording the body parameters and/or the current position information of the livestock and sending the body parameters and/or the current position information to a server through the Internet of things; the server is used for receiving the body parameters and/or the current position parameters, generating action tracks and/or motion frequencies of the livestock based on all the current position parameters, processing the body parameters, the action tracks and/or the motion tracks based on the neural network model to obtain body states of the livestock, and sending the body states to the user. Through this technical scheme, can in time master the health state of every livestock in extensive herd, and no longer rely on the personnel of breeding to look over one by one of every livestock to avoided bringing the loss for the raiser because can't in time master the health condition or other information of livestock.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic view of a livestock data analysis system according to an embodiment of the present application;
FIG. 2 is a flow chart of a data analysis method according to an embodiment of the present application;
fig. 3 is a block diagram of a data analysis apparatus according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Example one
Fig. 1 is a schematic diagram of a livestock data analysis system according to an embodiment of the present application.
As shown in fig. 1, the livestock data analysis system provided by the embodiment includes a server 100 and a plurality of electronic ear tags 200, each electronic ear tag is worn on an ear of a livestock to be monitored and has a wireless communication function, the server is directly in signal connection with each ear tag in a wireless manner through a base station, and the server can also upload data read from the corresponding ear tag through a reading device.
Each electronic ear tag in the embodiment can collect body parameters of corresponding livestock, such as body temperature, heart rate and the like, and can also collect current position information of the livestock, and the electronic ear tags automatically send the body parameters and the current position information to the server at regular intervals or through a collection instruction of the response server.
After receiving the current position information, the server can obtain information such as action tracks, motion frequency and the like of the livestock by processing a series of current position information. Then, the server processes one or more of the body information, the action track and the movement frequency based on the neural network model obtained by training in advance, thereby obtaining the body state of the livestock. The physical state may be a healthy state, such as whether diseased; it may also be a developmental state, such as dysplasia or well-developed; but also the oestrus state, whether the oestrus has been entered, etc.
It can be seen from the above technical solutions that the present embodiment provides a livestock data analysis system, which includes a server and a plurality of electronic ear tags connected with the server. Each electronic ear tag is used for being arranged on the ear of one livestock, and is used for acquiring and continuously recording the body parameters and/or the current position information of the livestock and sending the body parameters and/or the current position information to a server through the Internet of things; the server is used for receiving the body parameters and/or the current position parameters, generating action tracks and/or motion frequencies of the livestock based on all the current position parameters, processing the body parameters, the action tracks and/or the motion tracks based on the neural network model to obtain body states of the livestock, and sending the body states to the user. Through this technical scheme, can in time master the health state of every livestock in extensive herd, and no longer rely on the personnel of breeding to look over one by one of every livestock to avoided bringing the loss for the raiser because can't in time master the health condition or other information of livestock.
The neural network model in the application is obtained based on the following scheme training:
1. data collection
Based on different livestock living habits of each variety, some varieties of livestock belong to the good-movement type, and some varieties of livestock belong to the gentle and quiet type. Therefore, livestock of different varieties need to be modeled respectively, and the same machine learning algorithm can be used for modeling conveniently.
Our learning task is supervised, so each data needs to be calibrated, i.e. manually calibrated whether it is estrus or healthy. The data information required to be acquired mainly comprises the age, sex, body temperature, heartbeat frequency, position, movement track and movement frequency of the livestock and the current state information of the livestock. The age and sex information of the livestock can be directly obtained from a farmer, the state information of the livestock needs help of a professional to judge, the body temperature, the heartbeat frequency, the position, the motion track and the motion frequency information of the livestock can be directly obtained through an electronic ear, and the obtained information needs the assistance of the professional to carry out data calibration. The information to be calibrated here is mainly: body temperature range, heart rate range, movement frequency, movement range, etc. of the livestock under various conditions. The data set coverage is as wide as possible, and the data quantity of each category is balanced as much as possible, which is beneficial to the training of the model. And hierarchically dividing the acquired data into a training set and a testing set, wherein the ratio of the training set to the testing set is referred to 7: 3.
2. Model training
The learning task of the application belongs to a typical classification problem, and different kinds of livestock are classified into categories such as morbidity, health (no estrus), health (estrus) and the like according to data collected from the livestock, so that the purpose of early warning is achieved. The machine learning model is selected as the Support Vector Machine (SVM), and because boundary lines among different kinds of data are not necessarily straight lines, the mapping is carried out through a kernel function, so that the mapped data can be accurately divided through the SVM. After the learning algorithm is selected, learning the parameters of the model through a training data set, then evaluating the accuracy of the learned model through a test set, and stopping learning if the accuracy of the model meets the requirement to obtain the final training model.
3. Use of the model
After the model is trained, the model has the function of distinguishing the state of the livestock through the acquired data. The livestock variety can be distinguished through a CNN model in a patent 'livestock living body physical measurement device and an intelligent physical measurement system thereof', and then a corresponding SVM model is selected according to the livestock variety. Then the body temperature, heartbeat, position, movement track and movement frequency of the livestock collected by the electronic ear tag, the age, sex and other information of the livestock are input into the model, and the SVM model can output the state of the livestock, so that the purpose of intelligent monitoring is achieved.
Example two
Fig. 2 is a flowchart of a data analysis method according to an embodiment of the present application.
As shown in fig. 2, the mathematical analysis method provided by the present embodiment is applied to the livestock data analysis system in the previous embodiment, and in particular to the server of the livestock data analysis system, and the mathematical analysis method includes the following steps:
and S1, acquiring the body parameters and/or the current position information of the livestock.
Namely, receiving part or all of the body parameters and the current position information automatically sent by the electronic ear tag, or sending a data acquisition instruction to the electronic ear tag based on a request of a user, and then receiving part or all of the body parameters and the current position information sent by the electronic ear tag in response to the data acquisition instruction.
And S2, summarizing the current position information.
Namely, the action track and/or the movement frequency of the livestock are generated by summarizing a series of current position information.
And S3, processing the body parameters, the action track and the motion frequency.
That is, a part or all of the body parameters, the action trajectory and the motion frequency are processed based on a neural network model trained in advance, so as to obtain the generated body state. The neural network model is detailed above and will not be described in detail here.
By the method, the livestock data analysis system can obtain the body state of each livestock in the large-scale herd in time, so that a user can master the body state in time without depending on the livestock breeding personnel to check each livestock one by one, and the loss of the livestock breeding user caused by incapability of master the health state or other information of the livestock in time is avoided.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Although the operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the C language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer.
EXAMPLE III
Fig. 3 is a block diagram of a data analysis apparatus according to an embodiment of the present application.
As shown in fig. 3, the mathematical analysis apparatus provided by the present embodiment is applied to the livestock data analysis system in the previous embodiment, and in particular to the server of the livestock data analysis system, and the mathematical analysis apparatus includes a parameter acquisition module 10, a position processing module 20 and an analysis execution module 30.
The parameter disability module is used for acquiring body parameters and/or current position information of the livestock.
Namely, receiving part or all of the body parameters and the current position information automatically sent by the electronic ear tag, or sending a data acquisition instruction to the electronic ear tag based on a request of a user, and then receiving part or all of the body parameters and the current position information sent by the electronic ear tag in response to the data acquisition instruction.
The position processing module is used for summarizing the current position information.
Namely, the action track and/or the movement frequency of the livestock are generated by summarizing a series of current position information.
The analysis execution module is used for processing the body parameters, the action tracks and the motion frequency.
That is, a part or all of the body parameters, the action trajectory and the motion frequency are processed based on a neural network model trained in advance, so as to obtain the generated body state. The neural network model is detailed above and will not be described in detail here.
By the method, the livestock data analysis system can obtain the body state of each livestock in the large-scale herd in time, so that a user can master the body state in time without depending on the livestock breeding personnel to check each livestock one by one, and the loss of the livestock breeding user caused by incapability of master the health state or other information of the livestock in time is avoided.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of a unit does not in some cases constitute a limitation of the unit itself, for example, the first obtaining unit may also be described as a "unit obtaining at least two internet protocol addresses".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
While preferred embodiments of the present invention have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including the preferred embodiment and all changes and modifications that fall within the true scope of the embodiments of the present invention.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The technical solutions provided by the present invention are described in detail above, and the principle and the implementation of the present invention are explained in this document by applying specific examples, and the description of the above examples is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (8)
1. A livestock data analysis system, comprising a server and a plurality of electronic ear tags connected to said server, wherein:
each electronic ear tag is used for being configured on an ear of a livestock, and is used for acquiring and continuously recording body parameters and/or current position information of the livestock and sending the body parameters and/or the current position information to the server through the Internet of things;
the server is used for receiving the body parameters and/or the current position parameters, generating action tracks and/or motion frequencies of the livestock based on all the current position parameters, processing the body parameters, the action tracks and/or the motion tracks based on a neural network model to obtain body states of the livestock, and sending the body states to users.
2. The livestock data analysis system of claim 1, wherein said electronic ear tag is adapted to detect a body temperature and/or a heart rate of said livestock and to derive said physical parameter by combining said body temperature and/or said heart rate.
3. A data analysis method for use in the livestock data analysis system of claim 1 or 2, characterized in that said data analysis method comprises the steps of:
acquiring body parameters and/or current position information of livestock;
summarizing the current position information to obtain the action track and/or the movement frequency of the livestock;
and processing the body parameters, the action tracks and/or the motion frequency based on a neural network model to obtain the body state of the livestock.
4. A method of data analysis according to claim 3 wherein the physical parameter comprises body temperature and/or heart rate.
5. The data analysis method of claim 3, wherein the physical state comprises a healthy state, a developmental state, or an oestrus state.
6. A data analysis method for use in the livestock data analysis system of claim 1 or 2, wherein said data analysis means comprises:
a parameter acquisition module configured to acquire body parameters and/or current position information of the livestock;
the position processing module is configured to perform summary processing on the current position information to obtain action tracks and/or movement frequencies of the livestock;
an analysis execution module configured to process the body parameters, the action tracks and/or the motion frequency based on a neural network model to obtain the body state of the livestock.
7. The data analysis device of claim 6, wherein the physical parameter comprises body temperature and/or heart rate.
8. The data analysis device of claim 6, wherein the physical state comprises a healthy state, a developmental state, or an oestrus state.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116432907A (en) * | 2023-04-15 | 2023-07-14 | 亭曈(杭州)科技有限公司 | Livestock movement behavior analysis method and system based on deep learning |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116432907A (en) * | 2023-04-15 | 2023-07-14 | 亭曈(杭州)科技有限公司 | Livestock movement behavior analysis method and system based on deep learning |
| CN116432907B (en) * | 2023-04-15 | 2024-04-19 | 亭曈(杭州)科技有限公司 | Livestock movement behavior analysis method and system based on deep learning |
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